cd ../..

/code

%run "source/config/notebook_settings.py"
import os
import mlflow
from mlflow.tracking import MlflowClient
from helpsk.utility import read_pickle
import helpsk as hlp

from source.library.utilities import Timer, log_info, get_config

config = get_config()
mlflow_uri = config['MLFLOW']['URI']
log_info(f"MLFlow URI: {mlflow_uri}")

client = MlflowClient(tracking_uri=mlflow_uri)

2022-06-19 22:52:05 - INFO     | MLFlow URI: http://mlflow_server:1235

Get Latest Experiment Run from MLFlow

# Get the production model version and actual model
production_model_info = client.get_latest_versions(name=config['MLFLOW']['MODEL_NAME'], stages=['Production'])
assert len(production_model_info) == 1
production_model_info = production_model_info[0]
production_model = read_pickle(client.download_artifacts(
    run_id=production_model_info.run_id,
    path='model/model.pkl'
))
log_info(f"Production Model Version: {production_model_info.version}")

2022-06-19 22:52:05 - INFO     | Production Model Version: 1

# get experiment and latest run info
credit_experiment = client.get_experiment_by_name(name=config['MLFLOW']['EXPERIMENT_NAME'])
runs = client.list_run_infos(experiment_id=credit_experiment.experiment_id)
latest_run = runs[np.argmax([x.start_time for x in runs])]

yaml_path = client.download_artifacts(run_id=latest_run.run_id, path='experiment.yaml')
results = hlp.sklearn_eval.MLExperimentResults.from_yaml_file(yaml_file_name = yaml_path)

# get the best estimator from the BayesSearchCV
best_estimator = read_pickle(client.download_artifacts(
    run_id=latest_run.run_id,
    path='model/model.pkl'
))

best_estimator.model

Pipeline(steps=[('prep',
                 ColumnTransformer(transformers=[('numeric',
                                                  Pipeline(steps=[('imputer',
                                                                   TransformerChooser(transformer=SimpleImputer(strategy='median'))),
                                                                  ('scaler',
                                                                   TransformerChooser()),
                                                                  ('pca',
                                                                   TransformerChooser(transformer=PCA(n_components='mle')))]),
                                                  ['duration', 'credit_amount',
                                                   'installment_commitment',
                                                   'residence_since', 'age',
                                                   'existing_credi...
                                                   'savings_status',
                                                   'employment',
                                                   'personal_status',
                                                   'other_parties',
                                                   'property_magnitude',
                                                   'other_payment_plans',
                                                   'housing', 'job',
                                                   'own_telephone',
                                                   'foreign_worker'])])),
                ('model',
                 RandomForestClassifier(criterion='entropy', max_depth=70,
                                        max_features=0.1142268477118407,
                                        max_samples=0.5483119512487002,
                                        min_samples_leaf=8,
                                        min_samples_split=12, n_estimators=553,
                                        random_state=42))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Training & Test Data Info

client.download_artifacts(run_id=latest_run.run_id, path='x_train.pkl')

'/code/mlflow-artifact-root/1/27d636b527e246a08e584efac87e424b/artifacts/x_train.pkl'

with Timer("Loading training/test datasets"):
    X_train = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='x_train.pkl'))
    X_test = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='x_test.pkl'))
    y_train = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='y_train.pkl'))
    y_test = pd.pandas.read_pickle(client.download_artifacts(run_id=latest_run.run_id, path='y_test.pkl'))

2022-06-19 22:52:05 - INFO     | Timer Started: Loading training/test datasets
2022-06-19 22:52:05 - INFO     | Timer Finished: (0.02 seconds)

log_info(X_train.shape)
log_info(len(y_train))

log_info(X_test.shape)
log_info(len(y_test))

2022-06-19 22:52:05 - INFO     | (800, 20)
2022-06-19 22:52:05 - INFO     | 800
2022-06-19 22:52:05 - INFO     | (200, 20)
2022-06-19 22:52:05 - INFO     | 200

np.unique(y_train, return_counts=True)

(array([0, 1]), array([559, 241]))

np.unique(y_train, return_counts=True)[1] / np.sum(np.unique(y_train, return_counts=True)[1])

array([0.69875, 0.30125])

np.unique(y_test, return_counts=True)[1] / np.sum(np.unique(y_test, return_counts=True)[1])

array([0.705, 0.295])

Cross Validation Results

Best Scores/Params

log_info(f"Best Score: {results.best_score}")

2022-06-19 22:52:05 - INFO     | Best Score: 0.762641790401041

log_info(f"Best Params: {results.best_params}")

2022-06-19 22:52:05 - INFO     | Best Params: {'model': 'RandomForestClassifier()', 'max_features': 0.1142268477118407, 'max_depth': 70, 'n_estimators': 553, 'min_samples_split': 12, 'min_samples_leaf': 8, 'max_samples': 0.5483119512487002, 'criterion': 'entropy', 'imputer': "SimpleImputer(strategy='median')", 'scaler': 'None', 'pca': "PCA('mle')", 'encoder': 'OneHotEncoder()'}

# Best model from each model-type.
df = results.to_formatted_dataframe(return_style=False, include_rank=True)
df["model_rank"] = df.groupby("model")["roc_auc Mean"].rank(method="first", ascending=False)
df.query('model_rank == 1')

	rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	model	C	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	learning_rate	min_child_weight	subsample	colsample_bytree	colsample_bylevel	reg_alpha	reg_lambda	num_leaves	imputer	scaler	pca	encoder	model_rank
11	1	0.76	0.71	0.81	RandomForestClassifier()	NaN	0.11	70.00	553.00	12.00	8.00	0.55	entropy	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()	1.00
19	2	0.76	0.72	0.80	XGBClassifier()	NaN	NaN	1.00	896.00	NaN	NaN	NaN	NaN	0.03	8.00	0.80	0.91	0.83	0.00	1.41	NaN	SimpleImputer(strategy='median')	None	None	OneHotEncoder()	1.00
0	3	0.76	0.71	0.81	LogisticRegression()	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer()	StandardScaler()	None	OneHotEncoder()	1.00
24	4	0.76	0.73	0.79	LGBMClassifier()	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.51	0.68	NaN	5.68	42.57	50.00	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()	1.00
9	6	0.75	0.70	0.81	ExtraTreesClassifier()	NaN	0.03	84.00	1088.00	24.00	36.00	0.98	gini	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	SimpleImputer()	None	None	OneHotEncoder()	1.00

results.to_formatted_dataframe(return_style=True,
                               include_rank=True,
                               num_rows=500)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	model	C	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	learning_rate	min_child_weight	subsample	colsample_bytree	colsample_bylevel	reg_alpha	reg_lambda	num_leaves	imputer	scaler	pca	encoder
1	0.763	0.711	0.814	RandomForestClassifier()	<NA>	0.114	70.000	553.000	12.000	8.000	0.548	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()
2	0.761	0.719	0.802	XGBClassifier()	<NA>	<NA>	1.000	896.000	<NA>	<NA>	<NA>	<NA>	0.029	8.000	0.799	0.906	0.825	0.003	1.411	<NA>	SimpleImputer(strategy='median')	None	None	OneHotEncoder()
3	0.759	0.713	0.805	LogisticRegression()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	StandardScaler()	None	OneHotEncoder()
4	0.757	0.726	0.788	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.511	0.683	<NA>	5.684	42.574	50.000	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()
5	0.756	0.726	0.787	RandomForestClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
6	0.753	0.696	0.811	ExtraTreesClassifier()	<NA>	0.030	84.000	1,088.000	24.000	36.000	0.981	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
7	0.752	0.684	0.819	LogisticRegression()	0.001	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	MinMaxScaler()	None	OneHotEncoder()
8	0.750	0.711	0.789	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.797	0.700	<NA>	6.654	9.475	381.000	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
9	0.744	0.708	0.780	RandomForestClassifier()	<NA>	0.681	38.000	1,461.000	23.000	10.000	0.553	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
10	0.743	0.690	0.795	ExtraTreesClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
11	0.739	0.670	0.807	LogisticRegression()	23.327	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	StandardScaler()	None	OneHotEncoder()
12	0.738	0.705	0.772	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()
13	0.737	0.696	0.779	RandomForestClassifier()	<NA>	0.710	15.000	1,493.000	33.000	27.000	0.914	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	None	PCA('mle')	OneHotEncoder()
14	0.731	0.712	0.750	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.598	0.661	<NA>	12.533	35.084	348.000	SimpleImputer(strategy='most_frequent')	None	None	CustomOrdinalEncoder()
15	0.729	0.664	0.794	LGBMClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	0.845	0.453	<NA>	16.166	40.978	351.000	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()
16	0.728	0.703	0.752	XGBClassifier()	<NA>	<NA>	15.000	1,159.000	<NA>	<NA>	<NA>	<NA>	0.032	29.000	0.834	0.520	0.503	0.003	1.839	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
17	0.726	0.689	0.762	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	StandardScaler()	None	CustomOrdinalEncoder()
18	0.726	0.682	0.770	XGBClassifier()	<NA>	<NA>	5.000	1,218.000	<NA>	<NA>	<NA>	<NA>	0.115	2.000	0.545	0.648	0.852	0.123	1.165	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
19	0.725	0.699	0.752	RandomForestClassifier()	<NA>	0.740	14.000	1,645.000	5.000	43.000	0.741	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='most_frequent')	None	PCA('mle')	CustomOrdinalEncoder()
20	0.723	0.666	0.779	ExtraTreesClassifier()	<NA>	0.857	30.000	879.000	17.000	28.000	0.563	entropy	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	None	CustomOrdinalEncoder()
21	0.722	0.655	0.790	ExtraTreesClassifier()	<NA>	0.672	81.000	1,136.000	34.000	34.000	0.971	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	CustomOrdinalEncoder()
22	0.722	0.684	0.760	LogisticRegression()	0.000	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	StandardScaler()	PCA('mle')	CustomOrdinalEncoder()
23	0.722	0.658	0.786	ExtraTreesClassifier()	<NA>	0.781	50.000	590.000	35.000	47.000	0.846	gini	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer(strategy='median')	None	PCA('mle')	OneHotEncoder()
24	0.721	0.673	0.768	XGBClassifier()	<NA>	<NA>	3.000	682.000	<NA>	<NA>	<NA>	<NA>	0.152	2.000	0.698	0.940	0.817	0.009	2.086	<NA>	SimpleImputer()	None	PCA('mle')	CustomOrdinalEncoder()
25	0.718	0.695	0.740	XGBClassifier()	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	None	OneHotEncoder()

results.to_formatted_dataframe(query='model == "RandomForestClassifier()"', include_rank=True)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	max_features	max_depth	n_estimators	min_samples_split	min_samples_leaf	max_samples	criterion	imputer	pca	encoder
1	0.763	0.711	0.814	0.114	70.000	553.000	12.000	8.000	0.548	entropy	SimpleImputer(strategy='median')	PCA('mle')	OneHotEncoder()
2	0.756	0.726	0.787	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	<NA>	SimpleImputer()	None	OneHotEncoder()
3	0.744	0.708	0.780	0.681	38.000	1,461.000	23.000	10.000	0.553	gini	SimpleImputer(strategy='median')	None	CustomOrdinalEncoder()
4	0.737	0.696	0.779	0.710	15.000	1,493.000	33.000	27.000	0.914	gini	SimpleImputer(strategy='most_frequent')	PCA('mle')	OneHotEncoder()
5	0.725	0.699	0.752	0.740	14.000	1,645.000	5.000	43.000	0.741	entropy	SimpleImputer(strategy='most_frequent')	PCA('mle')	CustomOrdinalEncoder()

results.to_formatted_dataframe(query='model == "LogisticRegression()"', include_rank=True)

rank	roc_auc Mean	roc_auc 95CI.LO	roc_auc 95CI.HI	C	imputer	scaler	pca	encoder
1	0.759	0.713	0.805	<NA>	SimpleImputer()	StandardScaler()	None	OneHotEncoder()
2	0.752	0.684	0.819	0.001	SimpleImputer(strategy='median')	MinMaxScaler()	None	OneHotEncoder()
3	0.739	0.670	0.807	23.327	SimpleImputer(strategy='median')	StandardScaler()	None	OneHotEncoder()
4	0.726	0.689	0.762	0.000	SimpleImputer(strategy='median')	StandardScaler()	None	CustomOrdinalEncoder()
5	0.722	0.684	0.760	0.000	SimpleImputer(strategy='median')	StandardScaler()	PCA('mle')	CustomOrdinalEncoder()

BayesSearchCV Performance Over Time

results.plot_performance_across_trials(facet_by='model').show()

results.plot_performance_across_trials(query='model == "RandomForestClassifier()"').show()

---

Variable Performance Over Time

results.plot_parameter_values_across_trials(query='model == "RandomForestClassifier()"').show()

---

Scatter Matrix

# results.plot_scatter_matrix(query='model == "RandomForestClassifier()"',
#                             height=1000, width=1000).show()

---

Variable Performance - Numeric

results.plot_performance_numeric_params(query='model == "RandomForestClassifier()"',
                                        height=800)

results.plot_parallel_coordinates(query='model == "RandomForestClassifier()"').show()

---

Variable Performance - Non-Numeric

results.plot_performance_non_numeric_params(query='model == "RandomForestClassifier()"').show()

---

results.plot_score_vs_parameter(
    query='model == "RandomForestClassifier()"',
    parameter='max_features',
    size='max_depth',
    color='encoder',
)

---

# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='max_depth'
# )

# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='imputer'
# )

---

Best Model - Test Set Performance

test_predictions = best_estimator.predict(X_test)
test_predictions[0:10]

array([0.3733136 , 0.4118129 , 0.48404516, 0.35906406, 0.17344127,
       0.33008237, 0.17795812, 0.40549351, 0.21201897, 0.24426042])

evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37
)

evaluator.plot_actual_vs_predict_histogram()

evaluator.plot_confusion_matrix()

evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)

	Score	Dummy (prior)	Dummy (constant)	Explanation
AUC	0.794	0.500	0.500	Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate	0.593	0.000	1.000	59.3% of positive instances were correctly identified.; i.e. 35 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate	0.837	1.000	0.000	83.7% of negative instances were correctly identified.; i.e. 118 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate	0.163	0.000	1.000	16.3% of negative instances were incorrectly identified as positive; i.e. 23 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate	0.407	1.000	0.000	40.7% of positive instances were incorrectly identified as negative; i.e. 24 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value	0.603	0.000	0.295	When the model claims an instance is positive, it is correct 60.3% of the time; i.e. out of the 58 times the model predicted "Positive Class", it was correct 35 times; a.k.a precision
Negative Predictive Value	0.831	0.705	0.000	When the model claims an instance is negative, it is correct 83.1% of the time; i.e. out of the 142 times the model predicted "Negative Class", it was correct 118 times
F1 Score	0.598	0.000	0.456	The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC	0.642	0.295	0.295	Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy	0.765	0.705	0.295	76.5% of instances were correctly identified
Error Rate	0.235	0.295	0.705	23.5% of instances were incorrectly identified
% Positive	0.295	0.295	0.295	29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations	200	200	200	There are 200 total observations; i.e. sample size

evaluator.plot_roc_auc_curve().show()

<Figure size 720x444.984 with 0 Axes>

evaluator.plot_precision_recall_auc_curve().show()

evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()

evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()

evaluator.calculate_lift_gain(return_style=True)

	Gain	Lift
Percentile
5	0.17	3.39
10	0.25	2.54
15	0.32	2.15
20	0.42	2.12
25	0.53	2.10
30	0.61	2.03
35	0.68	1.94
40	0.75	1.86
45	0.76	1.69
50	0.80	1.59
55	0.80	1.45
60	0.88	1.47
65	0.88	1.36
70	0.92	1.31
75	0.93	1.24
80	0.97	1.21
85	0.98	1.16
90	1.00	1.11
95	1.00	1.05
100	1.00	1.00

---

Production Model - Test Set Performance

test_predictions = production_model.predict(X_test)
test_predictions[0:10]

array([0.388, 0.506, 0.724, 0.368, 0.056, 0.472, 0.076, 0.47 , 0.18 ,
       0.23 ])

evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37
)

evaluator.plot_actual_vs_predict_histogram()

evaluator.plot_confusion_matrix()

evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)

	Score	Dummy (prior)	Dummy (constant)	Explanation
AUC	0.823	0.500	0.500	Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate	0.746	0.000	1.000	74.6% of positive instances were correctly identified.; i.e. 44 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate	0.801	1.000	0.000	80.1% of negative instances were correctly identified.; i.e. 113 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate	0.199	0.000	1.000	19.9% of negative instances were incorrectly identified as positive; i.e. 28 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate	0.254	1.000	0.000	25.4% of positive instances were incorrectly identified as negative; i.e. 15 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value	0.611	0.000	0.295	When the model claims an instance is positive, it is correct 61.1% of the time; i.e. out of the 72 times the model predicted "Positive Class", it was correct 44 times; a.k.a precision
Negative Predictive Value	0.883	0.705	0.000	When the model claims an instance is negative, it is correct 88.3% of the time; i.e. out of the 128 times the model predicted "Negative Class", it was correct 113 times
F1 Score	0.672	0.000	0.456	The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC	0.662	0.295	0.295	Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy	0.785	0.705	0.295	78.5% of instances were correctly identified
Error Rate	0.215	0.295	0.705	21.5% of instances were incorrectly identified
% Positive	0.295	0.295	0.295	29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations	200	200	200	There are 200 total observations; i.e. sample size

evaluator.plot_roc_auc_curve().show()

<Figure size 720x444.984 with 0 Axes>

evaluator.plot_precision_recall_auc_curve().show()

evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()

evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()

evaluator.calculate_lift_gain(return_style=True)

	Gain	Lift
Percentile
5	0.14	2.71
10	0.24	2.37
15	0.37	2.49
20	0.49	2.46
25	0.54	2.17
30	0.66	2.20
35	0.71	2.03
40	0.75	1.86
45	0.80	1.77
50	0.83	1.66
55	0.85	1.54
60	0.86	1.44
65	0.90	1.38
70	0.93	1.33
75	0.95	1.27
80	0.97	1.21
85	0.98	1.16
90	1.00	1.11
95	1.00	1.05
100	1.00	1.00

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